ABSTRACT NUMBER: ESOC2026A1638 DEEP LEARNING-BASED ONSET TIME ESTIMATION IN ACUTE ISCHEMIC STROKE
Weiru Wang, Susanne Olthuis, Lisa Lavrova, Robert van Oostenbrugge, Charles Majoie, Ruisheng Su, Wim Van Zwam · 2026
Read ABSTRACT NUMBER: ESOC2026A1638 DEEP LEARNING-BASED ONSET TIME ESTIMATION IN ACUTE ISCHEMIC STROKE by Weiru Wang, Susanne Olthuis, Lisa Lavrova, Robert van Oostenbrugge, Charles Majoie, Ruisheng Su, Wim Van Zwam (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13146317.txt.
Description
Weiru Wang and co-authors build a tool to guess stroke age from brain scans in this 2026 study. Care choices for stroke hinge on how long ago the stroke began, yet that time is often unknown. Plain CT scans show early stroke marks only faintly, and expert reads of them are hard.
The team trained a deep learning model on more than sixteen hundred scans with known stroke ages from two Dutch study groups. The model sorts scans into three age bands split at key care cutoffs. It held up across scanner types and uneven group sizes. Scores beat older methods most on the bands that drive urgent care. The authors say the tool could back fast care choices when onset time is missing.
Catalog record
| CREATOR | Weiru Wang, Susanne Olthuis, Lisa Lavrova, Robert van Oostenbrugge, Charles Majoie, Ruisheng Su, Wim Van Zwam |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |